An adaptive predictor-corrector entry guidance law based on online parameter estimation

Due to the rapid improvement of the onboard computation capabilities, lots of novel entry guidance methods have appeared, of which numerical predictor-corrector algorithms have got a lot of attention and research. However, the properties of the predictor-corrector algorithms are vulnerable to the perturbation of the atmospheric density and aerodynamic parameters such as the lift and drag coefficient, which means that the algorithms highly depend on the model correctness. In this paper, an online identification method based on extended Kalman filter is used to estimate the uncertain parameters in reentry flight of X-33, which is of great value to reconfigure an auto-adaptive predictor-corrector guidance law. The Monte Carlo simulations show that the uncertainties in atmospheric density and aerodynamic parameters are estimated and an auto-adaptive guidance law is reconfigured successfully, which make great contributions to the satisfaction of the constraints in the presence of significant dispersions.